With the increasing soybean production in Brazil, and the demand for soybeans with high protein and oil content, it is essential to conduct an in-depth study of the constituents of this grain, which can vary according to genotypes and growing conditions. Therefore, the objective of this study was to classify soybean genotypes, cultivated in different environments and sowing seasons, according to their chemical composition and the spectrum generated by near-infrared spectroscopy (NIRS). For this purpose, artificial intelligence and its machine learning technique were employed. 10 soybean genotypes were used, sown in two sowing seasons and cultivated 7 cities in Rio Grande do Sul. The chemical composition of the samples was analyzed using the FOSS NIRS DS2500 equipment, selecting the band between 807 and 817 nm. The applied algorithms were J48, Random Forest, CVR, lBk, MLP, using the Resample filter. The Weka software, version 3.8.6, was employed for data mining. The IBk algorithm achieved the best performance, reaching 89% correct classification of attributes. From the Confusion Matrix, it was observed that all genotypes obtained results above 60/70 for correctly predicted values, highlighting the algorithms’ good performance. In the metrics, IBk achieved 0.89 Precision, Recall, and F-Measure, and 0.94 ROC Area. Thus, it was possible to classify the genotypes according to their chemical composition related to the data obtained in the spectral curve, sowing season, and environment, using artificial intelligence and machine learning.

A rice grain’s proximate composition determines its nutritional potential. Macronutrient quantification is essential to identify superior genotypes and direct breeding efforts to reach more people who are vulnerable. Conventional methods to determine proximate composition are highly accurate; however, they remain time-consuming, costly, and destructive. Near-infrared (NIR) spectroscopy enables proximate composition analysis in a non-destructive, rapid, inexpensive, and practical manner, providing results similar to well-established conventional methods. This study aimed to evaluate the feasibility of NIRs-based selection to identify more nutritious rice genotypes. A collection of 155 rice genotypes grown in Southern Brazil was used. After harvest, grains were hulled, polished, and milled. NIRs was used to determine moisture, starch, protein, fat, ash, and fiber contents in rice flour. It was possible to differentiate genotypes with higher and lower levels of the investigated components. Similar and distinct values were observed in comparison to other studies, indicating the accuracy of NIRs and the effect of genotype and environment, respectively. Starch is correlated negatively with protein and fat, preventing the identification of genotypes with high levels of these three components. PCA enabled the separation of the genotypes but highlighted the complexity of sample distribution. NIRs is an effective and accurate method to determine the proximate composition of rice, enabling the selection of more nutritious genotypes.

In search of a method that is effective in decontaminating insects during storage and that is not harmful to the environment, researchers have developed tests using decontamination with non-chemical methods. Among them, the modification of gas concentration during storage stands out, with injection mainly of N and CO₂. Both methods do not pose any risks to the environment or human health, and even so, they act as an insecticide to insect pests of stored grains, delaying grain degradation. In this chapter, non-Chemical methods for disinfestation of stored products were presented. <!----> <!----> <!----> <!---->

The growing demand for sustainable and nutritionally adequate protein sources has intensified interest in alternative plant proteins capable of supporting future food systems. However, many conventional plant proteins still present limitations related to allergenicity, digestibility, sensory quality, and functional performance, highlighting the need for novel sustainable ingredients. In this context, rice protein has emerged as a promising alternative due to its hypoallergenic nature, gluten-free composition, relatively high digestibility, suitability for specialized dietary applications, favorable techno-functional characteristics, and compatibility with sustainable food production systems. This review provides an integrated perspective on rice protein by critically discussing its nutritional quality, techno-functional properties, health-promoting potential, emerging food applications, and contribution to sustainable food systems, thereby addressing knowledge that has remained fragmented across previous reviews. Recent scientific studies addressing protein composition, digestibility, bioactive peptides, processing technologies, and applications in plant-based foods and nutraceutical systems were critically analyzed. Current evidence demonstrated that rice proteins and rice-derived peptides exhibit antioxidant, antihypertensive, anti-inflammatory, antidiabetic, and immunomodulatory activities, while recent advances in protein modification technologies have significantly improved solubility, emulsification, gelation, and structuring properties. Furthermore, the valorization of rice bran and broken rice supports circular economy strategies and sustainable food production. In this way, rice protein represents a strategic ingredient for the development of next-generation functional foods and sustainable health-oriented dietary systems.